Wrangle Data
espresso_summary <-
brewmaster_raw %>%
# Separate out name
separate(name, sep = ",", into = c("type", "roastery", "origin")) %>%
# parse json column
my_brew_data_json(note) %>%
# tidy decaf column
mutate(decaf = case_when(is.na(decaf) ~ F, T ~ T)) %>%
# tidy columns
mutate(createdAt = as_date(createdAt),
roast_date = as_date(roast_date),
age = createdAt - roast_date, ratio = `total weight`/dose) %>%
# rename columns
rename(total_time = `total time`, total_weight = `total weight`,
mean_flow = `average flowrate`, brew_data = `brew data`)
flow_data <-
espresso_summary %>%
my_brew_data_series(brew_data,
stall_tol = 0.1,
change_lag = 2) %>%
# reshape data to have weight and time in a single column
pivot_longer(cols = starts_with("t_"),
names_to = "time", values_to = "weight") %>%
# parse time and calc times
mutate_if(is.character, trimws) %>%
mutate(time = as.numeric(gsub("t_", "", time)) * 0.2,
weight = as.numeric(weight),
estimated_begin = estimated_first_flow * 0.2,
estimated_end = estimated_last_flow * 0.2,
flow_rate = (weight - dplyr::lag(weight))/0.2) %>%
group_by(id) %>%
mutate(smooth_flow = zoo::rollapply(flow_rate, 11, mean, fill = NA, na.rm = TRUE))
Weight Over Time
flow_data %>%
filter(!decaf) %>%
ggplot(aes(x = time, y = weight, group = id, colour = grind)) +
geom_line() +
scale_color_continuous(type = "viridis") +
scale_x_continuous(labels = function(x)paste0(x,"s")) +
scale_y_continuous("yield", labels = function(x)paste0(x, "g"))
Summary Stats
coffee_summary_extra <-
flow_data %>%
group_by(id, grind, origin, roastery, dose, age) %>%
summarise(max_entry = which.max(is.na(weight)),
final_weight = max(weight, na.rm = T),
estimated_begin = min(estimated_begin)) %>%
mutate(final_time = max_entry * 0.2,
age = as.numeric(age),
ratio = final_weight/dose) %>%
filter(final_weight > 20, final_weight < 50)
coffee_summary_extra %>%
ggplot(aes(final_time, grind, shape = origin, colour = age, size = ratio)) +
geom_jitter(width = 0, height = 0.075) +
scale_color_continuous(type = "viridis", labels = function(x) paste0(x, " dayss")) +
guides(size = guide_legend(reverse = TRUE)) +
scale_x_continuous("time", labels = function(x) paste0(x, "s"))
coffee_summary_extra %>%
ggplot(aes(dose, final_weight, colour = origin)) +
geom_point() +
scale_x_continuous("dose", labels = function(x)paste0(x, "g")) +
scale_y_continuous("yield", labels = function(x)paste0(x, "g"))
# coffee_summary_extra %>%
# ggplot(aes(ratio, final_time, colour = origin, size = grind)) +
# geom_point()
coffee_summary_extra %>%
ggplot(aes(final_time, ratio, colour = grind, size = age)) +
scale_color_continuous(type = "viridis") +
geom_point() +
facet_wrap(~origin) +
guides(size = guide_legend(reverse = TRUE)) +
scale_size_continuous(labels = function(x) paste0(x, " days")) +
scale_x_continuous("time", labels = function(x) paste0(x, "s"))
library(knitr)
library(kableExtra)
coffee_summary_extra %>%
select(roastery, origin, age, grind, dose, yield = final_weight, ratio, time = final_time) %>%
mutate(ratio = round(ratio, 2)) %>%
group_by(roastery, origin) %>%
summarise(mean_grind = mean(grind), mean_dose = mean(dose), mean_yield = mean(yield),
mean_ratio = mean(ratio), mean_time = mean(time)) %>%
arrange(roastery, origin) %>%
kable(digits = 2) %>%
kable_styling()
|
roastery
|
origin
|
mean_grind
|
mean_dose
|
mean_yield
|
mean_ratio
|
mean_time
|
|
Cloud Picker
|
Blend
|
2.64
|
18.00
|
38.39
|
2.13
|
31.28
|
|
Full Circle
|
Brazil
|
3.47
|
17.84
|
36.23
|
2.03
|
31.39
|
|
Full Circle
|
Guatemala
|
2.31
|
18.00
|
43.30
|
2.41
|
32.27
|
|
McCabes
|
Blend
|
4.50
|
17.60
|
49.82
|
2.83
|
27.40
|
|
McCabes
|
Brazil
|
4.18
|
14.68
|
44.25
|
3.05
|
29.65
|
|
McCabes
|
Organic
|
4.20
|
13.50
|
45.87
|
3.40
|
22.60
|
|
Square Mile
|
Blend
|
2.90
|
18.21
|
40.35
|
2.22
|
35.06
|
|
Upside
|
Blend
|
3.95
|
18.00
|
38.05
|
2.11
|
28.27
|
|
Upside
|
Brazil
|
4.00
|
18.03
|
37.30
|
2.07
|
28.52
|
|
Upside
|
Rwanda
|
3.74
|
17.95
|
39.00
|
2.17
|
26.84
|
Flow Rate
flow_data %>%
filter(smooth_flow > -2, smooth_flow < 8) %>%
mutate(age = as.numeric(age)) %>%
# # group_by(id, grind, origin, roastery, dose, age) %>%
# mutate(flow_rate = (weight - dplyr::lag(weight))/0.2) %>%
# group_by(id) %>%
# mutate(smooth_flow = zoo::rollapply(flow, 11, mean, fill = NA, na.rm = TRUE)) %>%
ggplot() +
geom_line(aes(x = time, y = smooth_flow, group = id, colour = age)) +
scale_color_continuous(type = "viridis") +
scale_y_continuous("flow rate", labels = function(x) paste0(x, "g/s")) +
geom_smooth(aes(x = time, y = smooth_flow), colour = "red")
Single Batch Over Time
single_batch <-
coffee_summary_extra %>%
filter(origin == "Brazil", roastery == "Full Circle", grind > 3.4) %>%
ggplot(aes(x = age, y = grind, size = ratio, colour = final_time,
group = 1, text = paste0("Dose: ", dose,
"<br>Yield: ", final_weight,
"<br>Ratio: ", round(ratio, 2),
"<br>Time: ", final_time))) +
geom_jitter(width = 0, height = 0.01, alpha = 0.9) +
scale_color_continuous(type = "viridis", labels = scales::number_format(suffix = "s")) +
guides(size = guide_legend(reverse = TRUE)) +
labs(x = "Days Since Roast", y = "Grind Setting", size = "Ratio", colour = "Shot Time")
plotly::ggplotly(single_batch, tooltip = "text")